Robust and reliable background subtraction is of critical importance for recognizing and tracking objects in many autonomous driving scenarios. There are many approaches for identifying foreground objects (or foreign objects) in a scene depending on whether the background is static or dynamic. For example, a simple inter-frame difference with a global threshold value may reveal foreign objects in a static background but may not be sensitive to phenomena that violate the basic assumptions of a static background subtraction, e.g., a rigorously fixed camera with a static noise-free background. In real-life scenarios, the illumination can change (gradually or suddenly), the background may contain moving objects (e.g., shadowing area change, trees shaken by the wind), and the camera may jitter and so forth. Existing background subtraction techniques have poor capability in handling variant illuminating conditions, causing false recognition of foreground objects when the illuminating condition is significantly different from a reference illumination. A particular application of background subtraction for identification of objects left in a vehicle is the vehicle interior monitoring for ride share/hailing service, where the illuminating conditions of the vehicle interior may be extremely different in a 24-hours period on locations such as the vehicle floor.